Merge bab8297000119678733d80e2cca1c1e1fe12e3f4 into c0d2f624c09dc18e709e37c2ad90c039a4eb72a2

whisper/transcribe.py

@@ -42,6 +42,7 @@ def transcribe(
     verbose: Optional[bool] = None,
     temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
     compression_ratio_threshold: Optional[float] = 2.4,
+    compression_ratio_hallucination_threshold: Optional[float] = 3,
     logprob_threshold: Optional[float] = -1.0,
     no_speech_threshold: Optional[float] = 0.6,
     condition_on_previous_text: bool = True,
@@ -76,6 +77,9 @@ def transcribe(
     compression_ratio_threshold: float
         If the gzip compression ratio is above this value, treat as failed
 
+    compression_ratio_hallucination_threshold: float
+        If the gzip compression ratio is above this value after all attempts to decode, treat as a hallucination and skip
+
     logprob_threshold: float
         If the average log probability over sampled tokens is below this value, treat as failed
 
@@ -218,6 +222,13 @@ def transcribe(
                 and decode_result.avg_logprob < logprob_threshold
             ):
                 needs_fallback = False  # silence
+            if (
+                compression_ratio_hallucination_threshold is not None
+                and decode_result.compression_ratio > compression_ratio_hallucination_threshold
+                and t == temperatures[-1]
+            ):
+            # Discard the segment
+                return None  # Skip to the next segment
             if not needs_fallback:
                 break
 
@@ -293,6 +304,14 @@ def transcribe(
                 decode_options["prompt"] = all_tokens[prompt_reset_since:]
 
             result: DecodingResult = decode_with_fallback(mel_segment)
+            if result is None:
+                if verbose:
+                    print(
+                        f"Discarding segment {format_timestamp(time_offset)} - {format_timestamp(time_offset + segment_duration)} "
+                        "due to high compression ratio."
+                    )
+                seek += segment_size  # Move to the next segment
+                continue  # Skip processing this segment
             tokens = torch.tensor(result.tokens)
 
             if no_speech_threshold is not None: